Vince Calhoun

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Vince D. Calhoun
VDC2.jpg
Born (1967-10-01) 1 October 1967 (age 56)
Nationality American
Alma mater University of Maryland, Baltimore County
Scientific career
Fields Electrical engineering
InstitutionsTri-institutional Center
Georgia Institute of Technology
University of New Mexico
Doctoral advisor Tülay Adalı

Vince Daniel Calhoun is an American engineer and neuroscientist. He directs the Tri-institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), a partnership between Georgia State University, Georgia Institute of Technology, and Emory University, and holds faculty appointments at all three institutions. He was formerly the President of the Mind Research Network and a Distinguished Professor of Electrical and Computer Engineering at the University of New Mexico.

Contents

Education

  1. B.S. in Electrical Engineering, University of Kansas, Lawrence, KS (1991)
  2. M.A. in Biomedical Engineering, Johns Hopkins University, Baltimore, MD (1993)
  3. M.S. in Information Systems, Johns Hopkins University, Baltimore, MD (1996)
  4. Ph.D. in Electrical Eng., University of Maryland, Baltimore County, Baltimore, MD (2002). [1]

Career

Calhoun is an expert on brain imaging acquisition and analysis and has created numerous algorithms for making sense of complex brain imaging data. He is the creator of the group independent component analysis algorithm, [2] which has become widely used for extracting 'networks' of coherent activity from functional magnetic resonance imaging (fMRI) data. He was also an early innovator in approaches to characterizing the dynamics of brain connectivity. [3] He has also developed techniques to link many different types of data, called 'data fusion' including various types of brain imaging (structural, functional, connectivity) with genomic and epigenomic data. [4] A key focus of Calhoun's work is the development of tool to identify brain imaging markers to help identify and potentially treat various brain disorders including schizophrenia, bipolar disorder, autism, Alzheimer's disease, and many more. [5]

Awards

Calhoun is a fellow of the Institute of Electrical and Electronics Engineers (IEEE), The American Association for the Advancement of Science (AAAS), The American Institute for Medical and Biological Engineering, [6] The American College of Neuropsychopharmacology, [7] and the International Society of Magnetic Resonance in Medicine (ISMRM). [8]

Related Research Articles

<span class="mw-page-title-main">Functional neuroimaging</span>

Functional neuroimaging is the use of neuroimaging technology to measure an aspect of brain function, often with a view to understanding the relationship between activity in certain brain areas and specific mental functions. It is primarily used as a research tool in cognitive neuroscience, cognitive psychology, neuropsychology, and social neuroscience.

Functional integration is the study of how brain regions work together to process information and effect responses. Though functional integration frequently relies on anatomic knowledge of the connections between brain areas, the emphasis is on how large clusters of neurons – numbering in the thousands or millions – fire together under various stimuli. The large datasets required for such a whole-scale picture of brain function have motivated the development of several novel and general methods for the statistical analysis of interdependence, such as dynamic causal modelling and statistical linear parametric mapping. These datasets are typically gathered in human subjects by non-invasive methods such as EEG/MEG, fMRI, or PET. The results can be of clinical value by helping to identify the regions responsible for psychiatric disorders, as well as to assess how different activities or lifestyles affect the functioning of the brain.

<span class="mw-page-title-main">Neuroimaging</span> Set of techniques to measure and visualize aspects of the nervous system

Neuroimaging is the use of quantitative (computational) techniques to study the structure and function of the central nervous system, developed as an objective way of scientifically studying the healthy human brain in a non-invasive manner. Increasingly it is also being used for quantitative research studies of brain disease and psychiatric illness. Neuroimaging is highly multidisciplinary involving neuroscience, computer science, psychology and statistics, and is not a medical specialty. Neuroimaging is sometimes confused with neuroradiology.

Connectomics is the production and study of connectomes: comprehensive maps of connections within an organism's nervous system. More generally, it can be thought of as the study of neuronal wiring diagrams with a focus on how structural connectivity, individual synapses, cellular morphology, and cellular ultrastructure contribute to the make up of a network. The nervous system is a network made of billions of connections and these connections are responsible for our thoughts, emotions, actions, memories, function and dysfunction. Therefore, the study of connectomics aims to advance our understanding of mental health and cognition by understanding how cells in the nervous system are connected and communicate. Because these structures are extremely complex, methods within this field use a high-throughput application of functional and structural neural imaging, most commonly magnetic resonance imaging (MRI), electron microscopy, and histological techniques in order to increase the speed, efficiency, and resolution of these nervous system maps. To date, tens of large scale datasets have been collected spanning the nervous system including the various areas of cortex, cerebellum, the retina, the peripheral nervous system and neuromuscular junctions.

<span class="mw-page-title-main">Default mode network</span> Large-scale brain network active when not focusing on an external task

In neuroscience, the default mode network (DMN), also known as the default network, default state network, or anatomically the medial frontoparietal network (M-FPN), is a large-scale brain network primarily composed of the dorsal medial prefrontal cortex, posterior cingulate cortex, precuneus and angular gyrus. It is best known for being active when a person is not focused on the outside world and the brain is at wakeful rest, such as during daydreaming and mind-wandering. It can also be active during detailed thoughts related to external task performance. Other times that the DMN is active include when the individual is thinking about others, thinking about themselves, remembering the past, and planning for the future.

Robert Turner is a British neuroscientist, physicist, and social anthropologist. He has been a director and professor at the Max Planck Institute for Human Cognitive and Brain Sciences in Leipzig, Germany, and is an internationally recognized expert in brain physics and magnetic resonance imaging (MRI). Coils inside every MRI scanner owe their shape to his ideas.

The Human Connectome Project (HCP) is a five-year project sponsored by sixteen components of the National Institutes of Health, split between two consortia of research institutions. The project was launched in July 2009 as the first of three Grand Challenges of the NIH's Blueprint for Neuroscience Research. On September 15, 2010, the NIH announced that it would award two grants: $30 million over five years to a consortium led by Washington University in St. Louis and the University of Minnesota, with strong contributions from University of Oxford (FMRIB) and $8.5 million over three years to a consortium led by Harvard University, Massachusetts General Hospital and the University of California Los Angeles.

Mark Steven Cohen is an American neuroscientist and early pioneer of functional brain imaging using magnetic resonance imaging. He currently is a Professor of Psychiatry, Neurology, Radiology, Psychology, Biomedical Physics and Biomedical Engineering at the Semel Institute for Neuroscience and Human Behavior and the Staglin Center for Cognitive Neuroscience. He is also a performing musician.

<span class="mw-page-title-main">Resting state fMRI</span> Type of functional magnetic resonance imaging

Resting state fMRI is a method of functional magnetic resonance imaging (fMRI) that is used in brain mapping to evaluate regional interactions that occur in a resting or task-negative state, when an explicit task is not being performed. A number of resting-state brain networks have been identified, one of which is the default mode network. These brain networks are observed through changes in blood flow in the brain which creates what is referred to as a blood-oxygen-level dependent (BOLD) signal that can be measured using fMRI.

The following outline is provided as an overview of and topical guide to brain mapping:

Dynamic functional connectivity (DFC) refers to the observed phenomenon that functional connectivity changes over a short time. Dynamic functional connectivity is a recent expansion on traditional functional connectivity analysis which typically assumes that functional networks are static in time. DFC is related to a variety of different neurological disorders, and has been suggested to be a more accurate representation of functional brain networks. The primary tool for analyzing DFC is fMRI, but DFC has also been observed with several other mediums. DFC is a recent development within the field of functional neuroimaging whose discovery was motivated by the observation of temporal variability in the rising field of steady state connectivity research.

Bruce Rosen is an American physicist and radiologist and a leading expert in the area of functional neuroimaging. His research for the past 30 years has focused on the development and application of physiological and functional nuclear magnetic resonance techniques, as well as new approaches to combine functional magnetic resonance imaging (fMRI) data with information from other modalities such as positron emission tomography (PET), magnetoencephalography (MEG) and noninvasive optical imaging. The techniques his group has developed to measure physiological and metabolic changes associated with brain activation and cerebrovascular insult are used by research centers and hospitals throughout the world.

<span class="mw-page-title-main">CONN (functional connectivity toolbox)</span>

CONN is a Matlab-based cross-platform imaging software for the computation, display, and analysis of functional connectivity in fMRI in the resting state and during task.

Arterial spin labeling (ASL), also known as arterial spin tagging, is a magnetic resonance imaging technique used to quantify cerebral blood perfusion by labelling blood water as it flows throughout the brain. ASL specifically refers to magnetic labeling of arterial blood below or in the imaging slab, without the need of gadolinium contrast. A number of ASL schemes are possible, the simplest being flow alternating inversion recovery (FAIR) which requires two acquisitions of identical parameters with the exception of the out-of-slice saturation; the difference in the two images is theoretically only from inflowing spins, and may be considered a 'perfusion map'. The ASL technique was developed by Alan P. Koretsky, Donald S. Williams, John A. Detre and John S. Leigh Jr in 1992.

Sharlene D. Newman is an American cognitive neuroscientist, executive director of the Alabama Life Research Institute at the University of Alabama (UA), Professor in the Department of Psychology at UA, and an adjunct professor in the Department of Psychological and Brain Sciences at Indiana University.

Functional MRI imaging methods have allowed researchers to combine neurocognitive testing with structural neuroanatomical measures, take into consideration both cognitive and affective paradigms, and subsequently create computer-aided diagnosis techniques and algorithms. Functional MRI has several benefits, such as its non-invasive quality, relatively high spatial resolution, and decent temporal resolution. One particular method used in recent research is resting-state functional magnetic resonance imaging, rs-fMRI. fMRI imaging has been applied to numerous behavioral studies for schizophrenia, the findings of which have hinted toward potential brain regions that govern key characteristics in cognition and affect.

Susan Whitfield-Gabrieli is an American scientist, psychologist/neuroscientist, academic and researcher. She is a professor of psychology, the Founding Director of the Biomedical Imaging Center at Northeastern University, Researcher in the Department of Psychiatry at Massachusetts General Hospital, Harvard Medical School and a Research Affiliate of McGovern Institute for Brain Research at Massachusetts Institute of Technology.

<span class="mw-page-title-main">Karla Miller</span> American neuroscientist and academic

Karla Loreen Miller is an American neuroscientist and professor of biomedical engineering at the University of Oxford. Her research investigates the development of neuroimaging techniques, with a particular focus on Magnetic Resonance Imaging (MRI), neuroimaging, diffusion MRI and functional magnetic resonance imaging. She was elected a Fellow of the International Society for Magnetic Resonance in Medicine in 2016.

References

  1. "Vince Calhoun | School of Electrical and Computer Engineering at the Georgia Institute of Technology". www.ece.gatech.edu. Retrieved 2020-04-09.
  2. Calhoun, V.D.; Adali, T.; Pearlson, G.D.; Pekar, J.J. (November 2001). "A method for making group inferences from functional MRI data using independent component analysis" (PDF). Human Brain Mapping. 14 (3): 140–151. CiteSeerX   10.1.1.467.1625 . doi:10.1002/hbm.1048. PMC   6871952 . PMID   11559959.
  3. Sakoğlu, U; Pearlson, G. D; Kiehl, K. A; Wang, Y. M; Michael, A. M; Calhoun, V. D (2010). "A method for evaluating dynamic functional network connectivity and task-modulation: application to schizophrenia". Magnetic Resonance Materials in Physics, Biology and Medicine. 23 (5–6): 351–66. doi:10.1007/s10334-010-0197-8. PMC   2891285 . PMID   20162320.
  4. Calhoun, V. D; Adali, T (2009). "Feature based fusion of medical imaging data". IEEE Transactions on Information Technology in Biomedicine. 13 (5): 711–20. doi:10.1109/TITB.2008.923773. PMC   2737598 . PMID   19273016.
  5. Arbabshirani, M. R; Plis, S; Sui, J; Calhoun, V. D (2017). "Single subject prediction of brain disorders in neuroimaging: Promises and pitfalls". NeuroImage. 145 (Pt B): 137–165. doi:10.1016/j.neuroimage.2016.02.079. PMC   5031516 . PMID   27012503.
  6. "Vince D. Calhoun, Ph.D. COF-1618 - AIMBE" . Retrieved 2020-04-07.
  7. "Membership". ACNP. Retrieved 2020-04-07.
  8. "Fellows of the Society". ISMRM. Retrieved 2020-04-09.